Traditionally, data analysis has been performed by systems (e.g. security systems, etc.) for determining whether such data is unwanted (e.g. Mal ware, etc.). Oftentimes, centrally managed systems are utilized for such data analysis, in which a central server analyzes data associated with multiple client devices. The centrally managed systems thus allow the central server to analyze data associated with one client device in view of other data received by the central server, such that a more comprehensive analysis may be performed.
However, such centrally managed systems have customarily been associated with various limitations. For example, large amounts of data communicated to a single central server from many client devices for analysis poses a manageability problem for the central server, in that the central server may generally be unable to manage all of the data. As another example, analyzing all data at a central server allows the central server to access even confidential data (e.g. banking information, passwords, etc.) associated with client devices that such client devices may not necessarily want disclosed.
As yet another example, use of a central server oftentimes results in scalability problems. In particular, the central server is generally required to receive and store large amounts of data communicated to such central server by client devices, which causes memory capacity and bandwidth consumption. In addition, processing resources of the central server are conventionally restricted and thus not necessarily capable of performing an analysis on all data communicated to the central server in a timely manner.
There is thus a need for addressing these and/or other issues associated with the prior art.